Computation of generic features for object classification

In this article we learn significant local appearance features for visual classes. Generic feature detectors are obtained by unsupervised learning using clustering. The resulting clusters, referred to as "classtons", identify the significant class characteristics from a small set of sample images. The classton channels mark these characteristics reliably using a probabilistic cluster representation. The classtons demonstrate good generalisation with respect to viewpoint changes and previously unseen objects. In all experiments, the classton channels of similar images have the same spatial relations. Learning of these relations allows to generate a classification model that combines the generalisation ability from the classtons and the discriminative power from the spatial relations.